Track 1: Theoretical Foundations of Cognition and AI
Focus: Core cognitive science principles, modeling paradigms, and theoretical frameworks that inspire AI.
Parallel Tracks: Foundations of Cognitive Modeling and AI
Advances in Cognitive Architectures
Learning, Memory, and Reasoning in AI
Neuroscience-Inspired Artificial Intelligence
Ethics, Consciousness, and Cognitive Philosophy in AI
Track 2: Machine Learning and Cognitive-Inspired AI
Focus: Cognitive mechanisms that inspire novel AI models and algorithms.
Parallel Tracks: Cognition-Inspired Machine Learning
Simulation and Evaluation of Cognitive Systems
Multimodal Cognition and Integration
Natural Language Understanding and Thought
Modeling Human Decision Making and Behavior
Track 3: Human-Centered and Social Cognition in AI
Focus: Interaction between humans and intelligent systems, and AI's modeling of social or emotional cognition.
Parallel Tracks: Human-Centered and Explainable AI
Emotion, Motivation, and Affective Intelligence
Social and Collaborative Cognition in AI
Cognitive Interfaces and Human-AI Interaction
Ethics, Trust, and Social Acceptability in AI
Track 4: Embodied Intelligence and Real-World Applications
Focus: Applying cognitive models to robotics, education, health, and real-world environments.
Parallel Tracks: Cognitive Robotics and Embodied Intelligence
Cognitive Development and Educational AI
AI for Cognitive and Mental Health
Cognitive Modeling in Games and Virtual Worlds
Benchmarking and Evaluation in Real-World Tasks
Track 5: Interdisciplinary and Future Perspectives
Focus: Cross-domain research, societal impact, and visionary work at the frontier of cognitive AI.
Parallel Tracks: Interdisciplinary Approaches to Cognitive AI
Future Challenges in Cognitive Modeling and AI
Cognitive Science of Large Language Models
Cognitive Models in Decision Support Systems
Philosophy of Mind and General Intelligence
Track 6: Cognitive Modeling Tools, Methods, and Simulation
Focus: Techniques and tools used to build, test, and evaluate cognitive models.
Parallel Tracks: Methods and Frameworks for Cognitive Modeling
Computational Simulation of Cognition
Behavioral Data Integration in Cognitive Systems
Validation, Benchmarking, and Reproducibility in Cognitive AI
Agent-Based and Multi-Agent Cognitive Simulations
Track 7: Brain, Biology, and Cognitive Computation
Focus: Biological and neurological underpinnings of cognitive processes and their AI counterparts.
Parallel Tracks: Brain-Inspired Computing and Neuromorphic Systems
Neural Correlates of Cognition and AI Modeling
Spiking Neural Networks and Cognitive Emulation
Computational Psychiatry and Neurological Disorders
Brain-Computer Interfaces and Cognitive AI Systems
Track 8: Cognitive AI in Education and Training
Focus: Applying cognitive models to learning environments, instructional tools, and assessment systems.
Parallel Tracks: AI-Based Cognitive Tutoring Systems
Modeling Learner Behavior and Knowledge Tracing
Adaptive Learning and Personalization through Cognitive AI
Educational Simulations and Virtual Agents
Cognitive Interventions for Learning Disabilities
Track 9: Engineering Cognitive Systems
Focus: Building deployable systems grounded in cognitive principles.
Parallel Tracks: Design and Implementation of Cognitive Agents
Real-Time Cognitive Processing in Embedded Systems
Software Architectures for Cognitive AI
Tools and Platforms for Building Cognitive Systems
Scalability and Optimization in Cognitive Model Deployment
Track 10: Societal Impact and AI Policy Informed by Cognition
Focus: Ethical, societal, and regulatory implications when AI systems model human cognition.
Parallel Tracks: Cognitive Bias, Fairness, and Inclusion in AI
Policy and Regulation for Cognitive Technologies
AI and Mental Health: Opportunities and Challenges
Public Understanding of Cognition and AI
Responsible Design of AI with Cognitive Insights